Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An iterative detection and decoding device, comprising: a signal detector, for generating a set of soft information (SI); a low-density parity check (LDPC) decoder which is an error-correction decoder, coupled to the signal detector, for iteratively decoding the set of SI and accordingly updating the set of SI to generate a set of updated SI; and an SI selector, coupled between the signal detector and the error-correction decoder, for selecting at least one SI from the set of updated SI when a binary summation of hard decisions of SI decoded from a same check node detector (CND) of the LDPC decoder is 0which is a predetermined constraint rule, wherein the signal detector further selectively generates a new set of SI according to a selection result generated from the SI selector.
An iterative decoding device improves error correction by selectively using soft information (SI). It includes a signal detector to produce initial SI values, and an LDPC (low-density parity check) decoder to iteratively refine these SI values. A key component is an SI selector placed between the detector and decoder. This selector checks each updated SI value against a constraint: whether the binary sum of hard decisions (0 or 1) from the same check node in the LDPC decoder equals zero. If this rule is met, the SI value is selected. The signal detector can then generate a new set of SI values based on these selections, focusing the decoding process.
2. The iterative detection and decoding device of claim 1 , wherein each SI included in the set of SI is a log likelihood ratio of a target bit.
The iterative decoding device as described above refines the soft information (SI) used during decoding. Each SI value represents a log-likelihood ratio (LLR) for a bit, indicating the probability of the bit being a 0 or 1. The LLR provides a measure of confidence in the bit's value, improving the accuracy of the LDPC decoder. By using LLRs as the SI, the system incorporates probabilistic information into the iterative decoding process.
3. The iterative detection and decoding device of claim 1 , wherein the predetermined constraint rule defines that hard decisions of SI fed back to a same variable node detector (VND) of the LDPC decoder should be the same.
The iterative decoding device described previously contains a constraint rule to enhance the soft information (SI) selection process. The selector checks if the hard decisions made from the SI values that are fed back to a same variable node in the LDPC decoder match. If the hard decisions are consistent (all 0s or all 1s), the SI values are deemed reliable. This helps to converge the iterative decoding process towards a valid solution by utilizing consistent information during refinement.
4. An iterative detection and decoding method, comprising: generating a set of soft information (SI); iteratively decoding the set of SI by employing a low-density parity check (LDPC) decoding scheme and accordingly updating the set of SI to generate a set of updated SI; selecting at least one SI from the set of updated SI when a binary summation of hard decisions of SI decoded from a same check node of the LDPC decoding scheme is 0which is a predetermined constraint rule; and selectively generating a new set of SI according to a selection result of the step of selecting at least one SI from the set of updated SI.
An iterative decoding method enhances error correction using soft information (SI) in a closed loop. The process starts by generating a set of SI values from a received signal. An LDPC decoding scheme is then applied iteratively to refine these SI values. A check is performed on the refined SI: if the binary sum of hard decisions from the same check node in the LDPC scheme equals zero, the SI is selected. Finally, a new set of SI values is selectively generated based on these selections, focusing the decoding process and improving error correction.
5. The iterative detection and decoding method of claim 4 , wherein each SI included in the set of SI is a log likelihood ratio of a target bit.
The iterative decoding method described in the previous process improves error correction by refining soft information (SI) used during decoding. Each SI value represents a log-likelihood ratio (LLR) for a target bit. By using LLRs, the method incorporates probabilistic information into the iterative decoding process, enhancing the accuracy and convergence of the LDPC decoder.
6. The iterative detection and decoding method of claim 4 , wherein the predetermined constraint rule defines that hard decisions of SI fed back to a same variable node of the LDPC decoding scheme should be the same.
The iterative decoding method uses a constraint rule to improve soft information (SI) selection. The method checks if the hard decisions derived from the SI values that are fed back to a same variable node in the LDPC scheme are the same. If the hard decisions are consistent, the SI values are considered reliable. This selection process ensures that only consistent information is used to update SI values to converge the iterative decoding process.
7. An iterative detection and decoding device, comprising: a signal detector, for generating a set of soft information (SI); a low-density parity check (LDPC) decoder which is an error-correction decoder, coupled to the signal detector, for iteratively decoding the set of SI and accordingly updating the set of SI to generate a set of updated SI; and an SI selector, coupled between the signal detector and the error-correction decoder, for selecting at least one SI from the set of updated SI when hard decisions of SI fed back to a same variable node detector (VND) of the LDPC decoder is the same and when a binary summation of hard decisions of SI decoded from a same check node detector (CND) of the LDPC decoder is 0, which is a predetermined constraint rule, wherein the signal detector further selectively generates a new set of SI according to a selection result generated from the SI selector.
An iterative decoding device improves error correction by applying dual constraint rule to soft information (SI) selection. It includes a signal detector to produce initial SI values, and an LDPC decoder to refine these SI values. An SI selector, between the detector and decoder, applies two constraints: first, hard decisions fed back to the same variable node must be the same; second, the binary sum of hard decisions from the same check node must be zero. If both constraints are met, the SI is selected. The signal detector generates a new set of SI values based on these selections, refining the decoding process.
8. An iterative detection and decoding method, comprising: generating a set of soft information (SI); iteratively decoding the set of SI by employing a low-density parity check (LDPC) decoding scheme and accordingly updating the set of SI to generate a set of updated SI; selecting at least one SI from the set of updated SI when hard decisions of SI fed back to a same variable node of the LDPC decoding scheme is the same and when a binary summation of hard decisions of SI decoded from a same check node detector (CND) of the LDPC decoder is 0; and selectively generating a new set of SI according to a selection result of the step of selecting at least one SI from the set of updated SI.
An iterative decoding method enhances error correction using soft information (SI) and applying dual constraint rule during selection. The method generates a set of SI values and iteratively refines these using an LDPC decoding scheme. The method selects at least one SI from the updated SI if *both* of these conditions are met: 1) the hard decisions derived from the SI that are fed back to a same variable node are the same; AND 2) the binary sum of hard decisions from the same check node equals zero. Based on this selection, a new set of SI values is generated to improve decoding.
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August 12, 2014
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